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The use of GPUs is incredibly helpful for many activities related to Machine Learning and Data Science, but correctly setting up your environment to leverage the processing power of these devices can often be a little confusing and time consuming, specially for people new to the field.
The goal of this post is to summarize all necessary steps to run TensorFlow on an NVIDIA GPU from a fresh Ubuntu 20.04 installation in May/2022.
This guide will cover the setup of:
The computer (notebook) used to develop this guide was equipped with an Intel® Core™ i7 CPU and an NVIDIA GeForce MX250 GPU. The following steps may vary slightly depending on your equipment.
To install the latest NVIDIA drivers you will need to:
These steps can be done with the following commands:
sudo apt-get purge nvidia-*
sudo apt-get update
sudo apt-get autoremove
apt search nvidia-driver
sudo apt install libnvidia-common-510
sudo apt install libnvidia-gl-510
sudo apt install nvidia-driver-510
sudo reboot
Check the pre-installation steps from the official documentation to make sure you have all necessary prerequisites.
Install Linux headers
sudo apt-get install linux-headers-$(uname -r)
Install CUDA Toolkit following the official documentation or running the commands bellow:
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda-repo-ubuntu2004-11-7-local_11.7.0-515.43.04-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-7-local_11.7.0-515.43.04-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2004-11-7-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda
Reboot to fix mismatched versions of drivers and libraries if you get the following error when running nvidia-smi
Failed to initialize NVML: Driver/library version mismatch
Download cnDNN
Download cuDNN.Install CUDNN following the official documentation or running the commands bellow for version 8.4.0.27:
sudo dpkg -i cudnn-local-repo-ubuntu2004-8.4.0.27_1.0-1_amd64.deb
sudo apt-key add /var/cudnn-local-repo-ubuntu2004-8.4.0.27/7fa2af80.pub
sudo apt-get update
sudo apt-get install libcudnn8
sudo apt-get install libcudnn8-dev
The steps 1-4 are enough to run TensorFlow locally on NVIDIA GPUs, but there are a few extra necessary steps in case you want to use the GPU in a Docker container with NVIDIA Container Toolkit.
Uninstall previous versions of Docker Engine
sudo apt-get purge docker-ce docker-ce-cli containerd.io docker-compose-plugin
sudo rm -rf /var/lib/docker
sudo rm -rf /var/lib/containerd
Install NVIDIA Container Toolkit following the official documentation or running the commands bellow:
curl https://get.docker.com | sh \
&& sudo systemctl --now enable docker
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
Test the installation with:
sudo docker run --rm --gpus all tensorflow/tensorflow:latest-gpu nvidia-smi
The result should look similar to this:
According to the official Docker documentation, in order to enable GPU access with Docker Compose, the following deploy information should be included to your docker-compose.yml file.
services:
test:
image: # your image
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: ["gpu"]